The Talairach transformation is widely used for analysis of neurological images. It involves identifying eight landmarks, which are used to define a coordinate system. The Talairach landmarks subdivide the brain into 12 cuboids, and the Talairach transformation is to warp the images within each cuboid linearly. In this way the brain images are normalised by a three-dimensional piece-wise linear warping. This scheme has several applications, in particular because it makes it possible to compare neurological images from different individuals. One improvement on this scheme, while following its conceptual rationale, is the improvement of the definitions of the landmarks, to give “modified Talairach landmarks” (as defined in the article “Modified Talairach Landmarks”, W. L. Nowinski, Acta Neurochirurgica, 2001, 143, p 1045-1057, In summary, the modified Talairach landmarks are derived by introducing three intercommissural distances: central, internal and tangential. Although these modified Talairach landmarks are conceptually equivalent to the original Talairach landmarks, they have several advantages and overcome some limitations of the original Talairach landmarks.
A principal advantage of the Talairach transform is its simplicity. Although numerous non-linear image registration techniques are known, in principle providing a higher accuracy, the non-linear techniques have limitations which make them difficult to use beyond a research environment. In particular, a prohibitively high computational time is required. Whereas the Talairach transformation can be performed in less than a second in a standard personal computer, some non-linear methods require days of computation. More fundamentally, the non-linear methods are conceptually complex, and must be treated as “black boxes”, which limits their clinical acceptance.
Furthermore, there is no established methodology for validation of the non-linear registration techniques.
One drawback of the Talairach transformation, however, is that the identification of the landmarks has not so far been automated reliably, so that when using conventional software which employs the Talairach transformation the landmarks still have to be identified by user interaction (e.g. R. W. Cox, “AFNI: Software for Analsysis and Visualization of Functional Magnetic Resonance Neuroimages”, Computer and Biomedical Research, 1996, 29, p 162-173). Quite apart from the time the interactive identification of the landmarks takes, different individuals are liable to locate the landmarks in slightly different positions, which reduces the robustness of the method.